- One factor ANOVA
- Git and GitHub
- Means tests in ANOVA
- Experimental Design
- Power analyses
- Multi-factor ANOVA
10/30/2018
From Langford, D. J.,et al. 2006. Science 312: 1967-1970
In words:
stretching = intercept + treatment
- The model statement includes a response variable, a constant, and an explanatory variable.
- The only difference with regression is that here the explanatory variable is categorical.
RNAseq_Data <- read.table('RNAseq_lip.tsv', header=T, sep='\t')
g1 <- RNAseq_Data$Gene01
Pop <- RNAseq_Data$Population
boxplot(g1~Pop, col=c("blue","green"))
Or, to plot all points:
stripchart(g1~Pop, vertical=T, pch=19, col=c("blue","green"),
at=c(1.25,1.75), method="jitter", jitter=0.05)
Pop_Anova <- aov(g1 ~ Pop)
summary(Pop_Anova)
R, lm assumes that all effects are fixedlme instead (part of the nlme package)dplyr functions.RNAseq_Data <- read.table("RNAseq.tsv", header=T, sep='')
x <- RNAseq_Data$categorical_var
y <- RNAseq_Data$continuous_var1
z <- RNAseq_Data$continuous_var2
contrasts(x) <- cbind(c(0, 1, 0, -1), c(2, -1, 0, -1), c(-1, -1, 3, -1)) round(crossprod(contrasts(x)), 2)
RNAseq_aov_fixed <- aov(y ~ x) plot(RNAseq_aov_fixed) boxplot(y ~ x) summary(RNAseq_aov_fixed, split = rnaseq_data_list)
perc <- read.table('perchlorate_data.tsv', header=T, sep='\t')
x <- perc$Perchlorate_Level
y <- log10(perc$T4_Hormone_Level)
MyANOVA <- aov(y ~ x)
summary (MyANOVA)
boxplot(y ~ x)
install.packages("multcomp")
library(multcomp)
summary(glht(MyANOVA, linfct = mcp(x = "Tukey")))
Survival of climbers of Mount Everest is higher for individuals taking supplemental oxygen than those who don’t. Why?
The goal of experimental design is to eliminate bias and to reduce sampling error when estimating and testing effects of one variable on another.